CCI.direction: Choose Direction for testing for the CCI test
In CCI: Computational Test for Conditional Independence

CCI.direction

R Documentation

Choose Direction for testing for the CCI test

Description

This function selects the best direction for the CCI test based on cross validation. For the condition Y || X | Z, the function return the recommended formula either Y ~ X | Z or X ~ Y | Z .

Usage

CCI.direction(
  formula,
  data,
  method = "rf",
  folds = 4,
  nrounds = 600,
  max_depth = 6,
  eta = 0.3,
  gamma = 0,
  colsample_bytree = 1,
  min_child_weight = 1,
  subsample = 1,
  poly = TRUE,
  degree = 3,
  interaction = TRUE,
  verbose = FALSE,
  ...
)

Arguments

`formula`	A formula object specifying the model to be fitted.
`data`	A data frame containing the variables specified in the formula.
`method`	A character string specifying the method to be used for model fitting. Options include "rf" (random forest), "xgboost" (XGBoost), "nnet" (neural network), "gpr" (Gaussian process regression), and "svm" (support vector machine).
`folds`	An integer specifying the number of folds for cross-validation. Default is 4.
`nrounds`	Integer. The number of rounds (trees) for methods like xgboost, ranger, and lightgbm. Default is 600.
`max_depth`	Integer. The maximum depth of the trees for methods like xgboost. Default is 6.
`eta`	Numeric. The learning rate for methods like xgboost. Default is 0.3.
`gamma`	Numeric. The minimum loss reduction required to make a further partition on a leaf node of the tree for methods like xgboost. Default is 0.
`colsample_bytree`	Numeric. The subsample ratio of columns when constructing each tree for methods like xgboost. Default is 1.
`min_child_weight`	Numeric. The minimum sum of instance weight (hessian) needed in a child for methods like xgboost. Default is 1.
`subsample`	Numeric. The proportion of the data to be used for subsampling. Default is 1 (no subsampling).
`poly`	Logical. If TRUE, polynomial terms of the conditioning variables are included in the model. Default is TRUE.
`degree`	Integer. The degree of polynomial terms to include if `poly` is TRUE. Default is 3.
`interaction`	Logical. If TRUE, interaction terms of the conditioning variables are included in the model. Default is TRUE.
`verbose`	Logical. If TRUE, prints additional information during the execution. Default is FALSE.
`...`	Additional arguments to be passed to the model fitting function.